Ethical AI in the Healthcare Sector: Investigating Key Drivers of Adoption through the Multi-Dimensional Ethical AI Adoption Model (MEAAM)

Ethical concerns drive AI adoption differently depending on context: The research found that normative concerns (justice, autonomy, dignity) most strongly influence whether healthcare organizations actually implement AI in daily operations, while overarching concerns (global and long-term implications) are more important for shaping broader governance strategies and systemic adoption decisions.

A comprehensive framework is needed to guide ethical AI in healthcare: The study introduces MEAAM, which identifies 13 critical ethical variables across four dimensions (Fair AI, Responsible AI, Explainable AI, and Sustainable AI), suggesting that piecemeal approaches to AI ethics are insufficient and healthcare organizations need multidimensional guidance.

Knowledge and transparency concerns matter but may be underweighted: While epistemic concerns related to system trustworthiness and transparency play a role in adoption decisions, the research indicates they are less influential than normative and overarching ethical concerns, potentially suggesting that healthcare organizations need better education on why these transparency issues are critical.

Healthcare professionals' perspectives reveal practical ethical priorities: The study surveyed healthcare professionals directly, finding that their ethical concerns—particularly around justice and moral obligations—are the primary drivers of whether they support AI adoption, indicating that frontline workers' ethical views should be central to implementation decisions.